• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.20 no.7
• /
• pp.1249-1254
• /
• 2016

Recently, there have been many researches which can achieve high data rate in multi-user massive MIMO networks while reducing the complexity in terms of both hardware and algorithm. In addition, many researches have been conduced to reduce the energy consumption in next generation mobile communication networks. In this paper, we thus investigated new transmit antenna selection scheme to achieve low computational complexity and enhance energy efficiency in multi-user massive MIMO networks. First, we introduced the optimal scheme based on Brute-Force searching to maximize the energy efficiency and then proposed new antenna selection scheme to dramatically reduce the computational complexity compared to the optimal scheme. As the number of transmit antennas increases, the complexity of the optimal scheme exponentially increases while the complexity of the proposed scheme linearly increases. Nevertheless, the energy efficiency performance gap between proposed and optimal schemes is not huge.

• Journal of Communications and Networks
• /
• v.15 no.4
• /
• pp.338-351
• /
• 2013

Multiple-input multiple-output (MIMO) communication may provide high spectral efficiency through the deployment of a very large number of antenna elements at the base stations. The gains from massive MIMO communication come from the use of multi-user MIMO on the uplink and downlink, but with a large excess of antennas at the base station compared to the number of served users. Initial work on massive MIMO did not fully address several practical issues associated with its deployment. This paper considers the impact of channel aging on the performance of massive MIMO systems. The effects of channel variation are characterized as a function of different system parameters assuming a simple model for the channel time variations at the transmitter. Channel prediction is proposed to overcome channel aging effects. The analytical results on aging show how capacity is lost due to time variation in the channel. Numerical results in a multicell network show that massive MIMO works even with some channel variation and that channel prediction could partially overcome channel aging effects.

• Journal of Communications and Networks
• /
• v.18 no.4
• /
• pp.649-657
• /
• 2016

In this paper, we investigate energy efficiency (EE) of the traditional co-located and the distributed massive multiple-input multiple-output (MIMO) systems. First, we derive an approximate EE expression for both the idealistic and the realistic power consumption models. Then an optimal energy-efficient remote access unit (RAU) selection algorithm based on the distance between the mobile stations (MSs) and the RAUs are developed to maximize the EE for the downlink distributed massive MIMO systems under the realistic power consumption model. Numerical results show that the EE of the distributed massive MIMO systems is larger than the co-located massive MIMO systems under both the idealistic and realistic power consumption models, and the optimal EE can be obtained by the developed energy-efficient RAU selection algorithm.

• Journal of Communications and Networks
• /
• v.15 no.4
• /
• pp.398-406
• /
• 2013

In this paper, we investigate two types of in-phase and quadrature-phase (IQ) data transfer methods for cloud multiple-input multiple-output (MIMO) network operation. They are termed "after-precoding" and "before-precoding". We formulate a cloud massive MIMO operation problem that aims at selecting the best IQ data transfer method and transmission strategy (beamforming technique, the number of concurrently receiving users, the number of used antennas for transmission) to maximize the ergodic sum-rate under a limited capacity of the digital unit-radio unit link. Based on our proposed solution, the optimal numbers of users and antennas are simultaneously chosen. Numerical results confirm that the sum-rate gain is greater when adaptive "after/before-precoding" method is available than when only conventional "after-precoding" IQ-data transfer is available.

• Journal of Communications and Networks
• /
• v.15 no.4
• /
• pp.352-361
• /
• 2013

Combining multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) with a massive number of transmit antennas (massive MIMO-OFDM) is an attractive way of increasing the spectrum efficiency or reducing the transmission energy per bit. The effectiveness of Massive MIMO-OFDM is strongly affected by the channel state information (CSI) estimation method used. The overheads of training frame transmission and CSI feedback decrease multiple access channel (MAC) efficiency and increase the CSI estimation cost at a user station (STA). This paper proposes a CSI estimation scheme that reduces the training frame length by using a novel pilot design and a novel unitary matrix feedback method. The proposed pilot design and unitary matrix feedback enable the access point (AP) to estimate the CSI of the signal space of all transmit antennas using a small number of training frames. Simulations in an IEEE 802.11n channel verify the attractive transmission performance of the proposed methods.

• International journal of advanced smart convergence
• /
• v.9 no.2
• /
• pp.58-67
• /
• 2020

In this paper, we propose a data-driven-based beam selection scheme for massive multiple-input and multiple-output (MIMO) systems in ultra-dense networks (UDN), which is capable of addressing the problem of high computational cost of conventional coordinated beamforming approaches. We consider highly dense small-cell scenarios with more small cells than mobile stations, in the millimetre-wave band. The analog beam selection for hybrid beamforming is a key issue in realizing millimetre-wave UDN MIMO systems. To reduce the computation complexity for the analog beam selection, in this paper, two deep neural network models are used. The channel samples, channel gains, and radio frequency beamforming vectors between the access points and mobile stations are collected at the central/cloud unit that is connected to all the small-cell access points, and are used to train the networks. The proposed machine-learning-based scheme provides an approach for the effective implementation of massive MIMO system in UDN environment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.2
• /
• pp.628-649
• /
• 2017

Security and resource-saving are both demands of the fifth generation (5G) wireless networks. In this paper, we study the secrecy spectrum efficiency (SSE) and secrecy energy efficiency (SEE) of a K-tier massive multiple-input multiple-output (MIMO) enabled heterogeneous cellular network (HetNet), in which artificial noise (AN) are employed for secrecy enhancement. Assuming (i) independent Poisson point process model for the locations of base stations (BSs) of each tier as well as that of eavesdroppers, (ii) zero-forcing precoding at the macrocell BSs (MBSs), and (iii) maximum average received power-based cell selection, the tractable lower bound expressions for SSE and SEE of massive MIMO enabled HetNets are derived. Then, the influences on secrecy oriented spectrum and energy efficiency performance caused by the power allocation for AN, transmit antenna number, number of users served by each MBS, and eavesdropper density are analyzed respectively. Moreover, the analysis accuracy is verified by Monte Carlo simulations.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.19 no.1
• /
• pp.77-82
• /
• 2015

Recently, multi-user massive MIMO (MU-Massive MIMO) network has attracted a lot of attention as a technology to accommodate explosively increasing mobile data traffic. However, the MU-Massive MIMO network causes a tremendous hardware complexity in a base station and computational complexity to select optimal set of users. In this paper, we thus propose a simple algorithm for selecting antennas and users while reducing the hardware and computational complexities simultaneously. The proposed scheme has a computational complexity of $O((N-S_a+1){\times}min(S_a,K))$, which is significantly reduced compared to the complexity of optimal scheme based on Brute-Force searching, $$O\left({_N}C_S_a\sum_{i=1}^{min(S_a,K)}_KC_i\right)$$, where N, $S_a$, and K denote the number of total transmit antennas, the number of selected antennas, and the number of all users, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38A no.8
• /
• pp.712-723
• /
• 2013

Recent exploration of smart-phone user is fueling the deployment of long term evolution (LTE) service that offers higher data rates service over 3G HSPA networks. In particular, Korea, mobile powerhouse, recently launched the service of LTE-Advanced (LTE-A) which is the latest release in LTE standard. Multiple-input-multiple-output (MIMO) technology is the one of key enablers for LTE and LTE-A for achieving high data rate. MIMO technology has received much attention since it is possible to achieve channel capacity in proportion to the number of antennas without increasing frequency and power. In this paper, we overview of the theoretical background of MIMO technology regarding from single-user MIMO, multiuser MIMO, and massive MIMO and design considerations to implement the communication system.

• Journal of Advanced Information Technology and Convergence
• /
• v.8 no.2
• /
• pp.13-25
• /
• 2018

The paper analyzes the sum spectral efficiency (SE) for a heterogeneous cellular network (HetNet) which has the backhaul, provided with wireless full-duplex massive multiple-input multiple-out (MIMO) with hardware distortions. We derive approximate expressions to obtain the uplink/downlink sum SE of the backhaul. The analytic results have been shown to be exact when compared to Monte Carlo simulations. From the analysis, it is shown that the desired signal and the hardware distortion noise have the same order. The sum SE generally improves when the number of receive antennas increases but degrades when the hardware quality reduces. A sum SE performance ceiling is introduced by the hardware quality level.